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    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Variogram Calculation Demonstration\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive workflow for calculating directional experimental variograms in 2D. \n",
    "\n",
    "* setting the variogram calculation parameters for identifying spatial data pairs \n",
    "\n",
    "This approach is essential for quantifying spatial continuity with sparsely sampled, irregular spatial data.\n",
    "\n",
    "I have more comprehensive workflows for variogram calculation:\n",
    "\n",
    "* [Experimental Variogram Calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n",
    "\n",
    "* [Determination of Major and Minor Spatial Continuity Directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "The variogram calculation parameters include:\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n",
    "\n",
    "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n",
    "\n",
    "* **bandwidth** is the maximum offset allowable from the lag vector \n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pm27995\\Anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\Users\\pm27995\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll\n",
      "C:\\Users\\pm27995\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"d:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
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       "      <td>1.996868</td>\n",
       "      <td>5590.417154</td>\n",
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       "      <th>1</th>\n",
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       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.145912</td>\n",
       "      <td>17.818143</td>\n",
       "      <td>3586.988513</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3732.114787</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.146088</td>\n",
       "      <td>16.717367</td>\n",
       "      <td>2534.551236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      X      Y  Facies  Porosity        Perm           AI\n",
       "0           0  100.0  900.0     0.0  0.101319    1.996868  5590.417154\n",
       "1           1  100.0  800.0     1.0  0.147676   10.711789  3470.845666\n",
       "2           2  100.0  700.0     1.0  0.145912   17.818143  3586.988513\n",
       "3           3  100.0  600.0     1.0  0.186167  217.109365  3732.114787\n",
       "4           4  100.0  500.0     1.0  0.146088   16.717367  2534.551236"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv(\"sample_data_MV_biased.csv\")           # read a .csv file in as a DataFrame\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\")\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>368.0</td>\n",
       "      <td>293.260870</td>\n",
       "      <td>169.058258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.500000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>439.500000</td>\n",
       "      <td>586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>368.0</td>\n",
       "      <td>499.565217</td>\n",
       "      <td>289.770794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>762.500000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>368.0</td>\n",
       "      <td>520.644022</td>\n",
       "      <td>277.412187</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>769.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>368.0</td>\n",
       "      <td>0.597826</td>\n",
       "      <td>0.491004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>368.0</td>\n",
       "      <td>0.127026</td>\n",
       "      <td>0.030642</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.103412</td>\n",
       "      <td>0.125842</td>\n",
       "      <td>0.148623</td>\n",
       "      <td>0.210258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>368.0</td>\n",
       "      <td>85.617362</td>\n",
       "      <td>228.362654</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>2.297348</td>\n",
       "      <td>10.377292</td>\n",
       "      <td>50.581288</td>\n",
       "      <td>1991.097723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>368.0</td>\n",
       "      <td>4791.736646</td>\n",
       "      <td>974.560569</td>\n",
       "      <td>1981.177309</td>\n",
       "      <td>4110.728374</td>\n",
       "      <td>4713.325533</td>\n",
       "      <td>5464.043562</td>\n",
       "      <td>7561.250336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean         std          min          25%  \\\n",
       "Unnamed: 0  368.0   293.260870  169.058258     0.000000   150.500000   \n",
       "X           368.0   499.565217  289.770794     0.000000   240.000000   \n",
       "Y           368.0   520.644022  277.412187     9.000000   269.000000   \n",
       "Facies      368.0     0.597826    0.491004     0.000000     0.000000   \n",
       "Porosity    368.0     0.127026    0.030642     0.041122     0.103412   \n",
       "Perm        368.0    85.617362  228.362654     0.094627     2.297348   \n",
       "AI          368.0  4791.736646  974.560569  1981.177309  4110.728374   \n",
       "\n",
       "                    50%          75%          max  \n",
       "Unnamed: 0   296.000000   439.500000   586.000000  \n",
       "X            500.000000   762.500000   990.000000  \n",
       "Y            539.000000   769.000000   999.000000  \n",
       "Facies         1.000000     1.000000     1.000000  \n",
       "Porosity       0.125842     0.148623     0.210258  \n",
       "Perm          10.377292    50.581288  1991.097723  \n",
       "AI          4713.325533  5464.043562  7561.250336  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                               # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the porosity and permeaiblity data to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101319</td>\n",
       "      <td>1.996868</td>\n",
       "      <td>5590.417154</td>\n",
       "      <td>-0.749088</td>\n",
       "      <td>-0.767247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.147676</td>\n",
       "      <td>10.711789</td>\n",
       "      <td>3470.845666</td>\n",
       "      <td>0.653263</td>\n",
       "      <td>0.017030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.145912</td>\n",
       "      <td>17.818143</td>\n",
       "      <td>3586.988513</td>\n",
       "      <td>0.611663</td>\n",
       "      <td>0.336607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.186167</td>\n",
       "      <td>217.109365</td>\n",
       "      <td>3732.114787</td>\n",
       "      <td>1.993601</td>\n",
       "      <td>1.211919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.146088</td>\n",
       "      <td>16.717367</td>\n",
       "      <td>2534.551236</td>\n",
       "      <td>0.628172</td>\n",
       "      <td>0.279461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      X      Y  Facies  Porosity        Perm           AI  \\\n",
       "0           0  100.0  900.0     0.0  0.101319    1.996868  5590.417154   \n",
       "1           1  100.0  800.0     1.0  0.147676   10.711789  3470.845666   \n",
       "2           2  100.0  700.0     1.0  0.145912   17.818143  3586.988513   \n",
       "3           3  100.0  600.0     1.0  0.186167  217.109365  3732.114787   \n",
       "4           4  100.0  500.0     1.0  0.146088   16.717367  2534.551236   \n",
       "\n",
       "       NPor     NPerm  \n",
       "0 -0.749088 -0.767247  \n",
       "1  0.653263  0.017030  \n",
       "2  0.611663  0.336607  \n",
       "3  1.993601  1.211919  \n",
       "4  0.628172  0.279461  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df['Porosity'], facecolor='red',bins=np.linspace(0.0,0.25,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)  \n",
    "plt.hist(df['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Nscore Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['Perm'], facecolor='red',bins=np.linspace(0.0,1000.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.0,1000.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['NPerm'], facecolor='blue',bins=np.linspace(-3.0,3.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Permeability (mD)'); plt.ylabel('Frequency'); plt.title('Nscore Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity and permeability to standard normal.\n",
    "\n",
    "#### Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the quantitative methods later.\n",
    "\n",
    "Let's plot the location maps of normal score transforms of porosity and permeability for all facies. We will also include a cross plot of the nscore permeability vs. porosity colored by facies to aid with comparison in spatial features between the porosity and permeability data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                                    # set the color map\n",
    "plt.subplot(131)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(132)                                        # location map of normal score transform of permeability\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(133)\n",
    "facies = df['Facies'].values +0.01                      # normal score porosity / permeability scatter plot color coded by facies\n",
    "plt.scatter(df['NPor'],df['NPerm'],c = facies,edgecolor = 'black',cmap = plt.cm.inferno)\n",
    "#plt.plot([-3,3],[-3,3],color = 'black')\n",
    "plt.xlabel(r'Nscore Porosity')\n",
    "plt.ylabel(r'Nscore Permeability')\n",
    "plt.title('Nscore Permeability vs. Porosity')\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=0.8, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experimental Variograms\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters. For example:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n",
    "\n",
    "#### Dashboard for Interactive Variogram Calculation\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating experimental variograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmath; import math\n",
    "\n",
    "# interactive calculation of the experimental variogram\n",
    "l = widgets.Text(value='                              Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "lag = widgets.FloatSlider(min = 20, max = 500, value = 100, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "lag.style.handle_color = 'gray'\n",
    "\n",
    "lag_tol = widgets.FloatSlider(min = 1, max = 500, value = 50, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "lag_tol.style.handle_color = 'gray'\n",
    "\n",
    "nlag = widgets.IntSlider(min = 1, max = 100, value = 10, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "nlag.style.handle_color = 'gray'\n",
    "\n",
    "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "azi.style.handle_color = 'gray'\n",
    "\n",
    "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 20, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'),continuous_update=False)\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "\n",
    "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting    \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth):     # function to take parameters, calculate variogram and plot\n",
    "#    text_trap = io.StringIO()\n",
    "#    sys.stdout = text_trap\n",
    "    tmin = -9999.9; tmax = 9999.9\n",
    "    lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=1.0)\n",
    "    plt.subplot(121)                                        # location map of normal score transform of porosity\n",
    "    GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "#     pt = cmath.rect(200, math.radians(azi-90))  \n",
    "#     x = pt.real+500  \n",
    "#     y = pt.imag+500\n",
    "    \n",
    "#     plt.plot([500,x],[500,y],color='red')\n",
    "    \n",
    "    plt.subplot(122)                                    # plot experimental variogram\n",
    "    scatter = plt.scatter(lags,gammas,color = 'darkorange',edgecolor='black',s = npps*0.05,label = 'Azimuth ' +str(azi))\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol < 90.0:\n",
    "        plt.title('Directional NSCORE Porosity Variogram - Azi ' + str(azi))\n",
    "    else:\n",
    "        plt.title('Omnidirectional NSCORE Porosity Variogram - Azi ' + str(azi))\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.annotate(r'Sill = $\\sigma^2$',[905,1.03])\n",
    "    plt.grid(True)\n",
    "    \n",
    "    legend = plt.legend(*scatter.legend_elements(\"sizes\", num=6),loc='upper left')\n",
    "    legend.set_title('Number of Pairs/20')\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.5, top=1.1, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Variogram Calculation Demostration\n",
    "\n",
    "* calculate omnidirectional and direction experimental variograms \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data.\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance\n",
    "\n",
    "* **number of lags** - number of lags in the experimental variogram\n",
    "\n",
    "* **bandwidth** - maximum departure from the lag vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e715b97c0814411f921f74482a923cb9",
       "version_major": 2,
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       "VBox(children=(Text(value='                              Variogram Calculation Interactive Demonstration, Mich…"
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     },
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     "output_type": "display_data"
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      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 3 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of vairogram calculation for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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